COCO [1]} is a very challenging object detection benchmark that has 80 object categories.
The union of 80K train images and a 35K subset of validation images are used as the training set and 5K subset of validation images (minival) is used as the evaluation set following [2]}, [3]}.
We use standard Average Precision(AP) for evaluation and adopt FPN [2]} as the detection method.
The \(2\times \)  setting released by Detectronhttps://github.com/facebookresearch/Detectron is used.
